CN110673130B - Moving target track tracking method based on track association - Google Patents

Moving target track tracking method based on track association Download PDF

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CN110673130B
CN110673130B CN201910863688.9A CN201910863688A CN110673130B CN 110673130 B CN110673130 B CN 110673130B CN 201910863688 A CN201910863688 A CN 201910863688A CN 110673130 B CN110673130 B CN 110673130B
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frame
target
distance
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CN110673130A (en
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王虹现
王开放
孙希平
张磊
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Xidian University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G01S13/583—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves and based upon the Doppler effect resulting from movement of targets
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G01S13/726—Multiple target tracking
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/418—Theoretical aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
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Abstract

The invention discloses a moving target track tracking method based on track association, which comprises the steps of firstly, carrying out pulse compression on an obtained FMCW radar intermediate frequency echo signal to obtain the distance information of a target; then, signal-to-noise ratio judgment is carried out to obtain a long-distance low signal-to-noise ratio signal and a short-distance high signal-to-noise ratio signal; performing CFAR detection and linear difference on the high signal-to-noise ratio signal, and performing multi-frame accumulation on the high signal-to-noise ratio signal; and finally, correlating the targets by using a track correlation processing method, and removing the high mutation to obtain a continuous moving target track. The invention greatly improves the detection precision, can calculate the height value of the target in real time, and marks the motion track of the target; meanwhile, the problems of discontinuous target track tracking and disordered target track are avoided.

Description

Moving target track tracking method based on track association
Technical Field
The invention belongs to the technical field of radar signal processing, and particularly relates to a moving target track tracking method based on track association.
Background
The radar can detect targets all the day by all weather because the radar is not blocked by fog, cloud and rain. Radar is an important sensor that is very sensitive to detection of target speed and distance and is therefore an ideal sensor for observing the distance of moving targets. Whereas frequency modulated continuous wave (Frequency Modulated Continuous Wave, FMCW) radar is widely used in the radar altimetry field due to its excellent performance and low cost.
In recent years, in modern wars, due to the development of various anti-radiation and stealth technologies and the application of related equipment, such as the measurement of the height of an aircraft or a missile, the measurement of the height of these targets is critical to the determination of the flight trajectory and the flight state of the aircraft or the missile itself. The height value is used for measuring an important parameter in the moving target, and the measured value of the height can reflect the track of the moving target in real time, so that the tracking and positioning of the target are realized.
In the traditional height measurement signal processing algorithm, the comparison of the target far and near signal to noise ratio is not judged, only a target detection method is used, and the target detection effect is not particularly ideal; in order to obtain a good target detection effect, the invention judges the signal-to-noise ratio of radar detection data according to the distance of the detection distance, wherein the distance is a low signal-to-noise ratio signal, and the distance is a high signal-to-noise ratio signal.
The existing algorithm in track tracking does not solve the problem of track height mutation caused by false alarm in detection results, thereby causing the problems of discontinuous track tracking, disordered track and the like.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a track tracking method of a moving target based on track association, which has higher detection precision, can calculate the height value of the target in real time and mark the moving track of the target; meanwhile, the problems of discontinuous target track tracking and disordered target track are avoided.
The technical idea of the invention is as follows: firstly, performing pulse compression on an acquired FMCW radar intermediate frequency echo signal to obtain distance information of a target; then, signal-to-noise ratio judgment is carried out to obtain a long-distance low signal-to-noise ratio signal and a short-distance high signal-to-noise ratio signal; performing Constant False Alarm Rate (CFAR) detection and linear difference on the high signal-to-noise ratio signal, and performing multi-frame accumulation on the high signal-to-noise ratio signal; and finally, correlating the targets by using a track correlation processing method, and removing the high mutation to obtain a continuous moving target track.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
A moving target track tracking method based on track association comprises the following steps:
step 1, acquiring echo data of a moving target by a radar, and performing pulse compression on the echo data to obtain an echo signal after pulse compression and distance information of the moving target;
step 2, according to the distance information of the moving object, carrying out signal-to-noise ratio judgment on the echo signals after pulse compression to respectively obtain long-distance low-signal-to-noise ratio signals and short-distance high-signal-to-noise ratio signals;
step 3, performing multi-frame incoherent accumulation processing on the long-distance low signal-to-noise ratio signal, selecting a target signal, and further obtaining a target detection value corresponding to the low signal-to-noise ratio;
step 4, CFAR detection is carried out on the short-distance high signal-to-noise ratio signal, and an initial detection signal of the target is obtained; performing linear difference on the initial detection signal of the target to obtain a target detection value corresponding to a high signal-to-noise ratio;
and 5, tracking the multi-target track by adopting a multi-target track association algorithm, and removing target track mutation to obtain a continuous moving target track.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention judges the signal-to-noise ratio of the echo data detected by the radar according to the distance between the detection distance and the near distance, the distance is a low signal-to-noise ratio signal, the near distance is a high signal-to-noise ratio signal, the detection precision is greatly improved, the height value of the target can be calculated in real time, and the motion track of the target is marked.
(2) According to the method, the distance mutation is removed through track association and multi-target tracking, and the target track mutation can be well solved, so that a good target tracking effect is achieved.
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The invention will now be described in further detail with reference to the drawings and to specific examples.
FIG. 1 is a flow chart of an implementation of the present invention;
fig. 2 is a flow chart of noise selection in snr decision in accordance with an embodiment of the present invention.
Fig. 3 is a diagram illustrating a process of selecting an index value of a detection threshold signal passing through a first layer in multi-frame incoherent accumulation according to an embodiment of the present invention.
FIG. 4 is a diagram showing the selection of five different accumulation directions in an embodiment of the present invention.
Fig. 5 is a diagram of a specific choice point implementation in five different accumulation directions for five frame times in an embodiment of the invention.
Fig. 6 is a schematic diagram of classifying the detection point targets into data clusters according to an embodiment of the present invention.
Fig. 7 is a specific flowchart of CFAR detection according to an embodiment of the present invention.
Fig. 8 is a schematic diagram of one interpolation interval in the linear difference in the embodiment of the present invention.
Fig. 9 is a schematic diagram of ten times the linear difference in an embodiment of the present invention.
FIG. 10 is a detailed flow chart of multi-objective track association in accordance with an embodiment of the present invention.
FIG. 11 is a schematic diagram of a target track start tag according to an embodiment of the present invention.
FIG. 12 is a schematic illustration of a track loss marker targeted for start-up in accordance with an embodiment of the present invention.
Fig. 13 is a simulated echo signal distance pulse pressure plot of an embodiment of the invention.
FIG. 14 is a diagram of simulated track following effects of an embodiment of the present invention.
Detailed Description
Embodiments and effects of the present invention are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, the track-related moving target track tracking method of the present invention comprises the following steps:
step 1, acquiring echo data of a moving target by a radar, and performing pulse compression on the echo data to obtain an echo signal after pulse compression and distance information of the moving target;
the method is implemented according to the following steps:
(1.1) transmitting signals by the FMCW radar, and carrying out echo admission on a target; let, the transmitted signal of the radar be:
Figure BDA0002200605490000041
/>
wherein j is an imaginary unit, f c For the starting frequency of the radar, γ is the chirp rate, t is the slow time,
Figure BDA0002200605490000042
is the fast time, i.e., the time within one transmit waveform; bandwidth b=γ×t of transmission signal r ，T r Is the repetition time of one transmitted signal.
(1.2) echo admission is carried out on a receiving channel of the FMCW radar; let a target be located in space, and its distance from the radar is R, then the echo signal of the target, i.e. the received signal, can be expressed as:
Figure BDA0002200605490000043
wherein R is (t) For the distance from the target to the first receiving antenna at time t, c is the speed of light.
(1.3) a mixer in the FMCW radar mixes the received signal with the transmitted signal to obtain an intermediate frequency echo signal; the mixing process is that
Figure BDA0002200605490000053
Where conj represents the conjugate of the signal.
The mixed intermediate frequency echo signal for one target is thus:
Figure BDA0002200605490000051
in the above equation, the first term in the exponential term is a mixed Doppler term, which contains Doppler information of a moving object, and is extracted when coherent processing of a plurality of chirp is performed. The second term is a distance term, which includes distance information from the target to the radar, and after the mixed signal is subjected to FFT, the obtained frequency value corresponds to the distance of the target. The third term is the mixed RVP (residual video phase) term, which has a very small value, much smaller than the first two terms, and can therefore be omitted in general.
(1.4) performing Fourier transform (FFT) on the intermediate frequency echo signals of the receiving channels to obtain echo signals after pulse pressure and corresponding frequency f b(t) ：
Figure BDA0002200605490000052
From the above, the intermediate frequency echo signal frequency is proportional to the target distance, so that the obtained frequency point corresponds to the target distance after Fourier transformation of the echo signal, thereby obtaining the distance information R of the target (t) 。
Step 2, according to the distance information of the moving object, carrying out signal-to-noise ratio judgment on the echo signals after pulse compression to respectively obtain long-distance low-signal-to-noise ratio signals and short-distance high-signal-to-noise ratio signals;
specifically, the target detection mode is determined by comparing the signal to noise ratio of the maximum value of the pulse pressure result after the chirp echo signal and the distance pulse pressure.
Determination of the signal-to-noise ratio of each frame of echo signals:
the signal-to-noise ratio judgment specifically comprises the following steps: selecting echo signal data after one frame of pulse compression, and dividing the length of the echo signal data after the frame of pulse compression in a distance dimension into a plurality of sections with equal intervals; then, the average value of each section of data in the distance dimension is calculated, and the minimum average value is selected as the signal noise value; specifically, as shown in fig. 2, if the maximum value of each frame of echo signal is smaller than 10 times of the signal noise value, the signal is judged to be a long-distance low signal-to-noise ratio signal, and otherwise, the signal is judged to be a short-distance high signal-to-noise ratio signal.
Illustratively, the length of each chirp signal in the distance dimension is divided into 10 segments of equally spaced lengths, as shown in fig. 2.
Step 3, performing multi-frame incoherent accumulation processing on the long-distance low signal-to-noise ratio signal, selecting a target signal, and further obtaining a target detection value corresponding to the low signal-to-noise ratio;
in the case where the target is far from the radar, since the radar echo intensity is inversely proportional to the distance to the power 4, the further the target echo intensity is weaker, the signal-to-noise ratio is lower, and false alarm or false alarm is liable to be caused. Therefore, a multi-frame accumulation method is used to enable multi-frame echo signals to be subjected to incoherent accumulation, and incoherent processing gain is obtained.
The multi-frame incoherent accumulation processing comprises the following specific steps:
(3.1) setting a first layer detection threshold: setting a first layer detection threshold value to be 2.5 times of a signal noise value, selecting each frame signal in the multi-frame signals, respectively comparing each frame signal with the first layer detection threshold value, if the frame signal is larger than the first layer detection threshold value, enabling the frame signal to pass through the first layer detection threshold value, marking the frame signal as a detected result, and recording the detected result of each frame and the number of corresponding detected results; the specific flow is shown in fig. 3.
(3.2) incoherent accumulation: firstly, adding and accumulating distance dimension data corresponding to continuous multi-frame signals passing through a first layer of detection threshold, finding out an index value with a detection result in the added and accumulated data, and recording the number of the detection results; and respectively carrying out accumulation searching in multiple directions on the first frame data by utilizing the index value with the detection result after addition and accumulation.
The different accumulation directions correspond to different prediction results, as shown in fig. 4, in which five different accumulation methods are shown, the larger the slope of the straight line is, the faster the target moving speed is represented, and the larger the height value is changed within 5 frame time.
The accumulation search is specifically:
first, determining the number of directions of accumulated searches; the present embodiment is 5 directions.
Secondly, taking the value of the index value with the detection result after addition and accumulation corresponding to the first frame as an initial incoherent accumulation value of each accumulation direction;
finally, determining the value of each accumulation direction;
when the frame number N of the multi-frame signal is even, the first accumulation direction value is the sum of N frame values in the same time dimension of the corresponding index value of the first frame; the second accumulation direction value is the index value corresponding to the front
Figure BDA0002200605490000071
Frame number and index value minus 1 are corresponding to +.>
Figure BDA0002200605490000072
The sum of the frame values; the third accumulation direction value is the sum of the index value minus 1 corresponding to the first to last frame value in turn; the fourth accumulation direction value is the index value corresponding to the first +.>
Figure BDA0002200605490000073
Frame value, index value minus 1 corresponding middle +.>
Figure BDA0002200605490000074
Frame number and index value minus 2 are corresponding to +.>
Figure BDA0002200605490000075
Adding the frame values; the fifth accumulation direction value is the index value corresponding to +.>
Figure BDA0002200605490000076
Frame value, index value plus 1 corresponding middle +.>
Figure BDA0002200605490000077
After the frame value is corresponding to the index value plus 2 +.>
Figure BDA0002200605490000078
Adding the frame values;
when the frame number N of the multi-frame signal is an odd number, the first accumulation direction value is the sum of N frame values in the same time dimension of the corresponding index value of the first frame; the second accumulating direction value is the cableLeading value corresponds to the front
Figure BDA0002200605490000079
Frame number and index value minus 1 are corresponding to +.>
Figure BDA00022006054900000710
The sum of the frame values; the third accumulation direction value is the sum of the index value minus 1 corresponding to the first to last frame value in turn; the fourth accumulation direction value is the index value corresponding to the first +.>
Figure BDA00022006054900000711
Frame value, index value minus 1 corresponding middle +.>
Figure BDA00022006054900000712
Adding 1 frame value after the frame value is subtracted by 2 from the index value; the fifth accumulation direction value is 1 frame value corresponding to the index value and 1 middle +.>
Figure BDA0002200605490000081
After the frame value is corresponding to the index value plus 2 +.>
Figure BDA0002200605490000082
Adding the frame values;
wherein N is a positive integer,
Figure BDA0002200605490000083
to round down the symbol +_>
Figure BDA0002200605490000084
To round the symbol up.
Illustratively, as shown in fig. 5, n=5, the value of each accumulation direction is determined as: the first accumulation direction value is the sum of 5 frame values in the same time dimension of the index value corresponding to the beginning of the first frame; the second accumulation direction value is the sum of the index value corresponding to the previous 3 frames and the index value minus the index value corresponding to the next 2 frames; the third accumulation direction value is the sum of the index value minus 1 corresponding to the first to last frame value in turn; the fourth accumulation direction value is the sum of the index value corresponding to the first 2 frames, the index value minus 1 corresponding to the middle 2 frames and the index value minus 2 corresponding to the last 1 frames; the fifth accumulation direction value is the sum of the index value corresponding to 1 frame value, the index value added with 1 corresponding to the middle 2 frame value and the index value added with 2 corresponding to the middle 2 frame value;
(3.3) setting a second layer detection threshold:
first, a second detection threshold is set as the average value of the frame signal passing the first detection threshold
Figure BDA0002200605490000085
Doubling;
then, comparing the maximum value of the accumulation values in the five accumulation directions with a second detection threshold value, and detecting an accumulation signal passing through a second layer detection threshold;
(3.4) setting a third layer detection threshold: adopting 0.95 times of the average value of the accumulated signals passing through the second layer detection threshold as a third layer detection threshold; comparing the accumulated signal passing through the second layer detection threshold with a third layer detection threshold value to detect a value passing through the third layer detection threshold;
(3.5) judging each cluster of data: as shown in fig. 6, among all values passing through the third layer detection threshold, values of which the distance between adjacent values is less than 2 distance units are classified as one cluster of data; searching a cluster where the maximum value of all the values passing through the third layer detection threshold is located, taking the cluster as a position cluster of the target signal, taking the first value in the position cluster of the target signal as a final detection target signal, namely the target detection value corresponding to the low signal-to-noise ratio.
Step 4, CFAR detection is carried out on the short-distance high signal-to-noise ratio signal, and an initial detection signal of the target is obtained; performing linear difference on the initial detection signal of the target to obtain a target detection value corresponding to a high signal-to-noise ratio;
performing CFAR detection and linear difference on the signal with the high signal-to-noise ratio in the step 2; when the radar detects a target, the radar signal detection generates false alarm due to strong clutter or interference, so that the control of the false alarm rate is an important problem of radar signal target detection. The constant false alarm detection is a classical target detection method in a radar system, and can obtain the bolt detection probability of more than 95% under the condition that the signal-to-noise ratio is more than 13 dB.
(4.1) performing CFAR detection on the short-distance high signal-to-noise ratio signal to obtain an initial detection signal of the target;
as shown in fig. 7, the specific process of constant false alarm detection is: firstly, selecting a detection unit from detection signals, and selecting a reference unit and a protection unit on two sides of the detection unit; in this embodiment, 3 protection units and 5 reference units are selected, the distance between the reference units is equal to the radar distance resolution, the detection unit compares with the adaptive threshold, and the signal passing through the adaptive threshold is the signal passing through the detection threshold, that is, the initial detection signal of the target;
the self-adaptive threshold is an average value of data summation of reference units on two sides of the detection unit under different weights, and represents radar environments on two sides of the detection unit; the method comprises the following steps: setting M reference units on two sides of the detection unit, wherein M is an even number, and two side values of the reference units are respectively
Figure BDA0002200605490000091
And->
Figure BDA0002200605490000092
Wherein x is i Data that is a reference cell; the adaptive threshold is z×k, where z=ax+by, k is a constant preset bY the system, and generally 1 is taken; a and b are weights of the reference units on both sides, respectively.
For example, in this embodiment, the number M of reference units on two sides of the detection unit is 10, the weights of the reference units are a=6.5 and b=0.3, and then the detection units are compared with the obtained threshold value to obtain a signal passing through the detection threshold.
(4.2) carrying out linear difference on the initial detection signal of the target to obtain a target detection value corresponding to the high signal-to-noise ratio;
the CFAR post-detection result is not so accurate due to the definition of the distance resolution, and thus the CFAR post-detection result is accurately processed using a linear difference algorithm. The difference is an important method of discrete function approximation, and the approximation of the function at other points can be estimated by using the value condition of the function at a limited number of points.
The linear difference is specifically:
firstly, inserting a plurality of nodes between two adjacent frames of signals at equal intervals, and solving signal data at each node by using a corresponding linear equation;
secondly, comparing the signal data at each node with a CFAR threshold, wherein the node signal data is the detected node signal data passing through the CFAR threshold, and the node signal data is larger than the CFAR threshold;
and finally, taking the first data in the node signal data passing through the CFAR threshold as a final detection signal result, namely the target detection value corresponding to the high signal-to-noise ratio.
Further, the linear equation is: connecting two adjacent nodes by straight lines to form a discount, namely a piecewise linear difference function;
as shown in fig. 8, two adjacent nodes are set to be (x 1 ，y 1 ) And (x) 2 ，y 2 ) The corresponding linear equation is:
Figure BDA0002200605490000101
in the above formula, when calculating x points, only two points around x are used, and the calculated amount is irrelevant to the number of nodes. For linear differences, the more the difference points, the better the difference effect, but the more the difference points, the more the calculation amount is increased. The 10-fold difference is selected in this embodiment, as shown in fig. 9, i.e., 10 points are inserted at equal intervals between signals of each frame.
And step 5, tracking the multi-target track by adopting a multi-target track association algorithm, and removing target track mutation to obtain a continuous moving target track.
In this embodiment, taking tracking 5 targets as an example, as shown in fig. 10, the specific steps are as follows:
(5.1) entering a value of the first frame data as a starting distance value of a first object, the first frame of the first object being marked 1;
(5.2) comparing the value of the incoming second frame data with the updated distance value of the first object, if the difference is less than the correlation threshold (e.g. 4, meaning that the values are relatively close), updating the distance value of the first object with the value of the incoming second frame data, otherwise, using the value as the starting value of the second object, and marking the second frame of the first object as 0;
and (5.3) analogizing, comparing the new value of the transmitted N frame data with the updated distance value of each target, if the number of the targets meeting the association condition is 1, updating the distance value of the target to be the transmitted new value, marking the new value as 1, and marking the rest targets as 0; if the number of the targets meeting the association condition is greater than 1, selecting the target with the largest distance value from the targets meeting the association condition for updating the distance value; if the target meeting the association condition does not exist, adding 1 to the number of the tracked targets, and taking the new value input as the initial value of the new target.
The association condition is that the difference value between the new value of the data of the N frame and the updated distance value of each target is smaller than an association threshold value.
In the specific process shown in fig. 10, for example, five targets are tracked, the first three targets have entered values, the updated values are 176, 152, 118, the new entered value is 174, the requirement of the first target is met, the first target height value is updated to 174, and the target 1 is marked as 1 at this frame time.
In the above process, since the target track height is in the descending state, the height of the next frame is definitely smaller than the height of the previous frame, so that if the difference value meets the condition of a plurality of targets at the same time, a target with a larger value is selected.
As shown in fig. 11, in the above process, if the sum of the four continuous frame track marks is greater than 3, it is determined that the track starts; as shown in fig. 12, if the four consecutive frames of track marks are all 0, it is determined that the track is lost.
In a radar multi-target tracking system, track association effects and performance of a multi-target tracking algorithm are key to influencing multi-target tracking accuracy. The invention improves track association effect and multi-target tracking performance.
Simulation experiment
The effect of the present invention can be further illustrated by the following simulation experiment.
1) Simulation conditions:
the simulation parameters of the pulse signals of the invention are shown in table 1:
TABLE 1 pulse Signal simulation parameters
Figure BDA0002200605490000121
2. Simulation content and result analysis:
simulating to establish a three-dimensional scene, wherein the target makes a diving motion at an angle of 40 degrees from top to bottom in the three-dimensional scene; the method of the invention is adopted to detect the radar direct wave on the moving target under the simulation condition to detect the ground target, and the target detection radar echo data is obtained, and the obtained distance pulse pressure result diagram is shown in figure 13. The distance pulse pressure result is processed by the method, the real-time height of the target is calculated, and the track of the target is drawn, as shown in fig. 14. As can be seen from fig. 14, the method of the invention can well extract the ground target signal in the radar echo through CFAR and multi-frame accumulation, can relatively accurately calculate the height of the moving object, can well detect and track the moving target track, and solves the problems of abrupt change of the target track and abrupt suspension of the track.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware associated with program instructions, where the foregoing program may be stored in a computer readable storage medium, and when executed, the program performs steps including the above method embodiments; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (8)

1. The track tracking method of the moving target based on track association is characterized by comprising the following steps of:
step 1, acquiring echo data of a moving target by a radar, and performing pulse compression on the echo data to obtain an echo signal after pulse compression and distance information of the moving target;
step 2, according to the distance information of the moving object, carrying out signal-to-noise ratio judgment on the echo signals after pulse compression to respectively obtain long-distance low-signal-to-noise ratio signals and short-distance high-signal-to-noise ratio signals;
in step 2, the signal-to-noise ratio decision is performed on the echo signal after pulse compression, which specifically includes:
selecting echo signal data after one frame of pulse compression, and dividing the length of the echo signal data after the frame of pulse compression in a distance dimension into a plurality of sections with equal intervals; then, the average value of each section of data in the distance dimension is calculated, and the minimum average value is selected as the signal noise value; if the maximum value of each frame of echo signal is smaller than 10 times of the signal noise value, judging the signal to be a long-distance low signal-to-noise ratio signal, otherwise, judging the signal to be a short-distance high signal-to-noise ratio signal;
step 3, performing multi-frame incoherent accumulation processing on the long-distance low signal-to-noise ratio signal, selecting a target signal, and further obtaining a target detection value corresponding to the low signal-to-noise ratio;
in step 3, the multi-frame incoherent accumulation processing is implemented according to the following steps:
(3.1) setting a first layer detection threshold: setting a first layer detection threshold value to be 2.5 times of a signal noise value, selecting each frame signal in the multi-frame signals, respectively comparing each frame signal with the first layer detection threshold value, if the frame signal is larger than the first layer detection threshold value, enabling the frame signal to pass through the first layer detection threshold value, marking the frame signal as a detected result, and recording the detected result of each frame and the number of corresponding detected results;
the signal noise value is: selecting echo signal data after one frame of pulse compression, and dividing the length of the echo signal data after the frame of pulse compression in a distance dimension into a plurality of sections with equal intervals; then, the average value of each section of data in the distance dimension is calculated, and the minimum average value is selected as the signal noise value;
(3.2) incoherent accumulation: firstly, adding and accumulating distance dimension data corresponding to continuous multi-frame signals passing through a first layer of detection threshold, finding out an index value with a detection result in the added and accumulated data, and recording the number of the detection results; respectively carrying out accumulation searching in multiple directions on the first frame data by utilizing the index value with the detection result after addition and accumulation;
(3.3) setting a second layer detection threshold:
firstly, setting a second detection threshold value to be 5 times of the average value of frame signals passing through the first detection threshold;
then, comparing the maximum value of the accumulation values in the five accumulation directions with a second detection threshold value, and detecting an accumulation signal passing through a second layer detection threshold;
(3.4) setting a third layer detection threshold: adopting 0.95 times of the average value of the accumulated signals passing through the second layer detection threshold as a third layer detection threshold; comparing the accumulated signal passing through the second layer detection threshold with a third layer detection threshold value to detect a value passing through the third layer detection threshold;
(3.5) judging each cluster of data: among all values passing through the third layer detection threshold, classifying values of which the distance between adjacent values is smaller than 2 distance units as a cluster of data; searching a cluster where the maximum value of all the values passing through the third layer detection threshold is located, taking the cluster as a position cluster of the target signal, taking the first value in the position cluster of the target signal as a final detection target signal, namely a target detection value corresponding to a low signal-to-noise ratio;
step 4, CFAR detection is carried out on the short-distance high signal-to-noise ratio signal, and an initial detection signal of the target is obtained; performing linear difference on the initial detection signal of the target to obtain a target detection value corresponding to a high signal-to-noise ratio;
and 5, tracking the multi-target track by adopting a multi-target track association algorithm, and removing target track mutation to obtain a continuous moving target track.
2. The track-related moving object track-following method according to claim 1, wherein step 1 is specifically implemented according to the following steps:
(1.1) transmitting signals by the FMCW radar, and carrying out echo admission on a target; the radar transmit signal is:
Figure FDA0004067482520000031
wherein j is an imaginary unit, f c For the starting frequency of the radar, γ is the chirp rate, t is the slow time,
Figure FDA0004067482520000032
is the fast time, i.e., the time within one transmit waveform; bandwidth b=γ×t of transmission signal r ，T r Is the repetition time of a transmitted signal;
(1.2) echo admission is carried out on a receiving channel of the FMCW radar; let a target be located in space, and its distance to the radar be R, then the echo signal of the target, i.e. the received signal, is expressed as:
Figure FDA0004067482520000033
wherein R is (t) The distance from the target at the moment t to the first receiving antenna is the light speed c;
(1.3) a mixer in the FMCW radar mixes the received signal with the transmitted signal to obtain an intermediate frequency echo signal; the mixing process is as follows:
Figure FDA0004067482520000034
wherein conj represents the conjugation of the signal;
thus, the mixed intermediate frequency echo signal for one target is:
Figure FDA0004067482520000035
the first term in the index term is a Doppler term after mixing, and Doppler information of a moving target is contained; the second term in the index term is a distance term and comprises distance information from a target to a radar; the third term in the exponential term is the residual video phase term after mixing, the value of the third term is far smaller than the first two terms, and the third term can be omitted;
(1.4) performing Fourier transform (FFT) on the intermediate frequency echo signals of the receiving channels to obtain echo signals after pulse pressure and corresponding frequency f b(t) ：
Figure FDA0004067482520000041
R in the formula (t) The distance from the target to the first receiving antenna at time t.
3. The track-related moving object track-following method according to claim 1, wherein the accumulation search is specifically:
first, determining the number of directions of accumulated searches;
secondly, taking the value of the index value with the detection result after addition and accumulation corresponding to the first frame as an initial incoherent accumulation value of each accumulation direction;
finally, the value for each accumulation direction is determined.
4. A track-related moving object tracking method according to claim 3, wherein the determining the value of each accumulation direction comprises the following specific steps:
when multiple frames of signalsWhen the number of frames N is even, the first accumulation direction value is the sum of the N frame values in the same time dimension of the index value corresponding to the beginning of the first frame; the second accumulation direction value is the index value corresponding to the front
Figure FDA0004067482520000042
Frame number and index value minus 1 are corresponding to +.>
Figure FDA0004067482520000043
The sum of the frame values; the third accumulation direction value is the sum of the index value minus 1 corresponding to the first to last frame value in turn; the fourth accumulation direction value is the index value corresponding to the first +.>
Figure FDA0004067482520000044
Frame value, index value minus 1 corresponding middle +.>
Figure FDA0004067482520000045
Frame number and index value minus 2 are corresponding to +.>
Figure FDA0004067482520000046
Adding the frame values; the fifth accumulation direction value is the index value corresponding to +.>
Figure FDA0004067482520000047
Frame value, index value plus 1 corresponding middle +.>
Figure FDA0004067482520000051
After the frame value is corresponding to the index value plus 2 +.>
Figure FDA0004067482520000052
Adding the frame values;
when the frame number N of the multi-frame signal is an odd number, the first accumulation direction value is the sum of N frame values in the same time dimension of the corresponding index value of the first frame; the second accumulation direction value is the index value corresponding to the front
Figure FDA0004067482520000053
Frame number and index value minus 1 are corresponding to +.>
Figure FDA0004067482520000054
The sum of the frame values; the third accumulation direction value is the sum of the index value minus 1 corresponding to the first to last frame value in turn; the fourth accumulation direction value is the index value corresponding to the first +.>
Figure FDA0004067482520000055
Frame value, index value minus 1 corresponding middle +.>
Figure FDA0004067482520000056
Adding 1 frame value after the frame value is subtracted by 2 from the index value; the fifth accumulation direction value is 1 frame value corresponding to the index value and 1 middle +.>
Figure FDA0004067482520000057
After the frame value is corresponding to the index value plus 2 +.>
Figure FDA0004067482520000058
Adding the frame values;
wherein N is a positive integer,
Figure FDA0004067482520000059
to round down the symbol +_>
Figure FDA00040674825200000510
To round the symbol up.
5. The track-related moving object track-following method according to claim 1, wherein in step 4, the CFAR detection specifically includes: firstly, selecting a detection unit from detection signals, and selecting a reference unit and a protection unit on two sides of the detection unit; the interval between the reference units is equal to the radar range resolution; then, comparing the detection unit with the self-adaptive threshold, wherein the signal passing through the self-adaptive threshold is the signal passing through the detection threshold, namely the initial detection signal of the target;
the self-adaptive threshold is an average value of data summation of the reference unit under different weights, and specifically comprises the following steps: setting M reference units on two sides of the detection unit, wherein M is an even number, and two side values of the reference units are respectively
Figure FDA00040674825200000511
And
Figure FDA00040674825200000512
wherein x is i Data that is a reference cell; the adaptive threshold is z×k, where z=ax+by, k is a constant preset bY the system, and generally 1 is taken; a and b are weights of the reference units on both sides, respectively.
6. The track-related moving object track-following method according to claim 1, wherein the linear difference is specifically:
firstly, inserting a plurality of nodes between two adjacent frames of signals at equal intervals, and solving signal data at each node by using a corresponding linear equation;
secondly, comparing the signal data at each node with a CFAR threshold, wherein the node signal data is the detected node signal data passing through the CFAR threshold, and the node signal data is larger than the CFAR threshold;
and finally, taking the first data in the node signal data passing through the CFAR threshold as a final detection signal result, namely the target detection value corresponding to the high signal-to-noise ratio.
7. The track-related moving object track-following method according to claim 6, wherein the linear equation is: connecting two adjacent nodes by using a straight line to form a straight line, namely a piecewise linear difference function; specifically:
setting two adjacent nodesRespectively (x) 1 ,y 1 ) And (x) 2 ,y 2 ) The corresponding linear equation is:
Figure FDA0004067482520000061
wherein f (x) is a function corresponding to a straight line formed by connecting two adjacent nodes.
8. The track-association-based moving object track tracking method as claimed in claim 1, wherein the step 5 is implemented as follows:
(5.1) entering a value of the first frame data as a starting distance value of a first object, the first frame of the first object being marked 1;
(5.2) the value of the incoming second frame data is differed from the updated distance value of the first object, if the difference is smaller than the correlation threshold, the distance value of the first object is updated by the value of the incoming second frame data, otherwise, the value is taken as the initial value of the second object, and the second frame of the first object is marked as 0;
and (5.3) analogizing, comparing the new value of the transmitted N frame data with the updated distance value of each target, if the number of the targets meeting the association condition is 1, updating the distance value of the target to be the transmitted new value, marking the new value as 1, and marking the rest targets as 0; if the number of the targets meeting the association condition is greater than 1, selecting the target with the largest distance value from the targets meeting the association condition for updating the distance value; if the target meeting the association condition does not exist, adding 1 to the tracked target number, and taking the new value transmitted into the target number as the initial value of the new target;
the association condition is that the difference value between the new value of the data of the N frame and the updated distance value of each target is smaller than an association threshold value.
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CN111398948B (en) * 2020-04-08 2021-12-10 成都汇蓉国科微系统技术有限公司 Maneuvering small target track association method under strong clutter background
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CN113532422B (en) * 2021-07-12 2022-06-21 哈尔滨工程大学 Multi-sensor track fusion method based on distance map and data cleaning
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007240317A (en) * 2006-03-08 2007-09-20 Toshiba Corp Detection distance calculation method, and radar system using this
CN101872484A (en) * 2010-06-30 2010-10-27 西安电子科技大学 Method for detecting self-adaptive growth of a plurality of dim target tracks in image domain
CN104330780A (en) * 2014-09-25 2015-02-04 中国地质大学（武汉） Object detection method and apparatus based on self-adaptive frequency domain noncoherent accumulation
CN107797101A (en) * 2017-10-24 2018-03-13 哈尔滨工业大学 Track initiation method based on a mark multidimensional filtering under dense clutter environment

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140266860A1 (en) * 2013-03-14 2014-09-18 Gaddi BLUMROSEN Method and system for activity detection and classification

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007240317A (en) * 2006-03-08 2007-09-20 Toshiba Corp Detection distance calculation method, and radar system using this
CN101872484A (en) * 2010-06-30 2010-10-27 西安电子科技大学 Method for detecting self-adaptive growth of a plurality of dim target tracks in image domain
CN104330780A (en) * 2014-09-25 2015-02-04 中国地质大学（武汉） Object detection method and apparatus based on self-adaptive frequency domain noncoherent accumulation
CN107797101A (en) * 2017-10-24 2018-03-13 哈尔滨工业大学 Track initiation method based on a mark multidimensional filtering under dense clutter environment

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
Moving target coherent integration method fornon-coherent short pulse radar;Haibo Wang et al.;《IET Radar, Sonar & Navigation》;20190601;第13卷(第6期);第953-960页 *
Radar Detection during Tracking with Constant Track False Alarm Rate;Hongliang Liu et al.;《2014 International Radar Conference》;20150316;第1-5页 *
Research of Non-coherent Detection in Jamming Environment;Chengquan Luo et al.;《IET International Radar Conference 2013》;20131010;第1-4页 *
基于探测概率的雷达网反隐身开关机模型;徐会法等;《雷达科学与技术》;20070430;第5卷(第2期);第95-99页 *
星载雷达弱目标长时间积累算法研究;位寅生等;《系统工程与电子技术》;20071031;第29卷(第10期);第1638-1642页 *

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